Evolving embodied intelligence from materials to machines

David Howard*, Agoston E. Eiben, Danielle Frances Kennedy, Jean Baptiste Mouret, Philip Valencia, Dave Winkler

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Natural lifeforms specialize to their environmental niches across many levels, from low-level features such as DNA and proteins, through to higher-level artefacts including eyes, limbs and overarching body plans. We propose ‘multi-level evolution’, a bottom-up automatic process that designs robots across multiple levels and niches them to tasks and environmental conditions. Multi-level evolution concurrently explores constituent molecular and material building blocks, as well as their possible assemblies into specialized morphological and sensorimotor configurations. Multi-level evolution provides a route to fully harness a recent explosion in available candidate materials and ongoing advances in rapid manufacturing processes. We outline a feasible architecture that realizes this vision, highlight the main roadblocks and how they may be overcome, and show robotic applications to which multi-level evolution is particularly suited. By forming a research agenda to stimulate discussion between researchers in related fields, we hope to inspire the pursuit of multi-level robotic design all the way from material to machine.

Original languageEnglish
Pages (from-to)12-19
Number of pages8
JournalNature Machine Intelligence
Volume1
Issue number1
Early online date7 Jan 2019
DOIs
Publication statusPublished - Jan 2019

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